A Survey of Different Prediction Models & Role of Artificial Neural Networks for Natural Gas Consumption
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Survey Paper | Computer Science & Engineering | India | Volume 4 Issue 11, November 2015 | Popularity: 6.6 / 10


     

A Survey of Different Prediction Models & Role of Artificial Neural Networks for Natural Gas Consumption

Prabodh K Pradhan, Sunil Dhal


Abstract: It is very important fact that natural gas is now a very significant element of human beings day to day life. Local Distribution Companies (LDCs) are forecasting their customers' natural gas demand with different traditional and advanced methodologies. However the forecasting needs to be accurate to mitigate the demand and supply. A significant error on a single day can cost a lot to the customers of the LDC. Different attempts have been made to predict the natural gas prediction accurately by different research paper. A better and more realistic energy forecast is essential for the policy makers and LDC owners / managers when futuristic decisions for the next epoch. As this upsurge in energy usage may posture a threat to economic development in the nation, Therefore, the policy makers need an accurate prediction of natural gas consumption to meet the demand and needs of the people. This paper looks at the brief description of each method and techniques described in different research papers.


Keywords: Neural Network, prediction, ARIMAX, Gas consumption, Mathematical model


Edition: Volume 4 Issue 11, November 2015


Pages: 1325 - 1328



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Prabodh K Pradhan, Sunil Dhal, "A Survey of Different Prediction Models & Role of Artificial Neural Networks for Natural Gas Consumption", International Journal of Science and Research (IJSR), Volume 4 Issue 11, November 2015, pp. 1325-1328, https://www.ijsr.net/getabstract.php?paperid=NOV151383, DOI: https://www.doi.org/10.21275/NOV151383

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